observabilityaudit-loggingmcpopentelemetry
Records AI agent activity in a SHA-256 hash chain and supports verification, signed export, and OpenTelemetry ingestion.
Quick Install
Automated & IDE SetupCopy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
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Manual Client & Custom JSON ConfigExpand JSON ▾
Client Config & Setup
Choose your client or environment
Target File:
💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Overview
Use cases
Record MCP tool calls for audit review
Verify that agent event history was not altered
Inspect MCP sessions in an observability dashboard
Ingest OpenTelemetry GenAI traces alongside MCP activity
Key features
SHA-256 hash-chained audit events
Audit-chain verification
Signed JSON audit export
MCP client integration
OpenTelemetry GenAI trace ingestion
SQLite or Postgres storage
Capabilities & Tool Schemas
Inspect callable tools, capabilities, and parameters exposed to AI agents by Agentlens.
Extracted Tool Capabilities
SHA-256 hash-chained audit events
Audit-chain verification
Signed JSON audit export
MCP client integration
OpenTelemetry GenAI trace ingestion
SQLite or Postgres storage
How Agentlens works
What agentkitai/agentlens MCP server does
The agentkitai/agentlens MCP server is the MCP-facing component of AgentLens, an observability system for AI agents. It connects MCP clients, including Claude Desktop and Cursor, to an AgentLens instance over HTTP. The integration records MCP tool calls, sessions, and related events for later inspection through the AgentLens API and dashboard.
AgentLens is designed around an append-only audit trail. Each event is linked to the preceding event with a SHA-256 hash. Verification can identify when a record has been altered, removed, or reordered. The server also supports the broader AgentLens workflow for preserving an audit record and producing a signed JSON export through the platform's verification export endpoint.
How it works
An MCP client sends activity through the AgentLens MCP integration. The MCP component forwards that activity to the AgentLens server, where the ingest and query components process it and store it in SQLite or Postgres. A dashboard is available from the server for viewing collected activity.
The platform can also ingest OpenTelemetry traces that use GenAI semantic conventions. OTLP HTTP traces sent to the AgentLens endpoints are mapped into events such as language-model calls, responses, tool calls, embeddings, and agent invocations. Traces become sessions, or use gen_ai.conversation.id when that identifier is present. These events receive the same hash-chain treatment as MCP-originated records.
Setup and configuration
The agentkitai/agentlens MCP server is distributed as an npm package named @agentkitai/agentlens-mcp. The repository also documents a separate AgentLens server package, @agentkitai/agentlens-server, which provides the receiving service and dashboard. A local AgentLens instance can run with SQLite and zero configuration through Docker or the server package.
For a quick local deployment, the documented Docker command publishes port 3400. Production-style deployments can use the repository's Docker Compose configuration with Postgres, Redis, authentication, and TLS settings. The MCP client must be configured to connect to the relevant AgentLens deployment; the provided material does not specify a complete client configuration block or MCP-specific credential variable.
Tools and capabilities
The agentkitai/agentlens MCP server supports:
- Recording MCP tool calls, sessions, and agent events.
- Sending MCP activity to an AgentLens HTTP service.
- Preserving events in a SHA-256 hash-chained audit log.
- Verifying whether the recorded chain remains intact.
- Working alongside OpenTelemetry GenAI trace ingestion.
- Inspecting collected activity through the AgentLens API and web dashboard.
AgentLens can reconstruct model-call cost from model identifiers and token usage for supported OpenTelemetry GenAI traces. Its documented integrations also include Python auto-instrumentation, an OpenClaw plugin, and SDKs, but those are separate from the MCP package.
Limitations and notes
The MCP package is only one part of the deployment: it needs an AgentLens server to receive and store the activity. The material does not list the individual MCP tool names or provide a standalone storage mode for the MCP package.
The quick local Docker example disables authentication and uses a development JWT secret, so it is intended for local trials. Shared or production deployments should use authentication and a real JWT secret. OpenTelemetry authentication can be enabled for OTLP endpoints with OTLP_AUTH_TOKEN.
The agentkitai/agentlens MCP server is open source under the MIT license. The repository describes its audit trail as suitable for EU AI Act Article 12 record-keeping, but compliance decisions still depend on the complete deployment and operating process.
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Adoption & maintenance
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GitHub stars
23
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npm downloads
349
Package downloads in the last 30 days.
Last commit
12d ago
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Directory activity
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Scanned 21d ago via OSV.dev · @agentkitai/agentlens-server (npm)
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